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Generalizable Person Re-identification by Domain-Invariant Mapping Network

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https://ieeexplore.ieee.org/document/8953522
Original languageEnglish
Title of host publication2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages719-728
Number of pages10
ISBN (Electronic)978-1-7281-3293-8
ISBN (Print)978-1-7281-3294-5
DOIs
Publication statusPublished - 9 Jan 2020
Event2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition - Long Beach, United States
Duration: 16 Jun 201920 Jun 2019
http://cvpr2019.thecvf.com/

Publication series

Name
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

Conference2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2019
CountryUnited States
CityLong Beach
Period16/06/1920/06/19
Internet address

Abstract

We aim to learn a domain generalizable person reidentification (ReID) model. When such a model is trained on a set of source domains (ReID datasets collected from different camera networks), it can be directly applied to any new unseen dataset for effective ReID without any model updating. Despite its practical value in real-world deployments, generalizable ReID has seldom been studied. In this work, a novel deep ReID model termed Domain-Invariant Mapping Network (DIMN) is proposed. DIMN is designed to learn a mapping between a person image and its identity classifier, i.e., it produces a classifier using a single shot. To make the model domain-invariant, we follow a meta-learning pipeline and sample a subset of source domain training tasks during each training episode. However, the model is significantly different from conventional meta-learning methods in that: (1) no model updating is required for the target domain, (2) different training tasks share a memory bank for maintaining both scalability and discrimination ability, and (3) it can be used to match an arbitrary number of identities in a target domain. Extensive experiments on a newly proposed large-scale ReID domain generalization benchmark show that our DIMN significantly outperforms alternative domain generalization or meta-learning methods.

Event

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition

16/06/1920/06/19

Long Beach, United States

Event: Conference

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